False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
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Author(s)

  • Lilun Du
  • Xu Guo
  • Wenguang Sun
  • Changliang Zou

Detail(s)

Original languageEnglish
Pages (from-to)607-621
Journal / PublicationJournal of the American Statistical Association
Volume118
Issue number541
Online published24 Aug 2021
Publication statusPublished - 2023
Externally publishedYes

Abstract

We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to control the FDR. The SDA filter substantially outperforms the Knockoff method in power under moderate to strong dependence, and is more robust than existing methods based on asymptotic p-values. We first develop finite-sample theories to provide an upper bound for the actual FDR under general dependence, and then establish the asymptotic validity of SDA for both the FDR and false discovery proportion control under mild regularity conditions. The procedure is implemented in the R package sdafilter. Numerical results confirm the effectiveness and robustness of SDA in FDR control and show that it achieves substantial power gain over existing methods in many settings.

© 2021 American Statistical Association

Research Area(s)

  • Empirical distribution, Integrative multiple testing, Moderate deviation theory, Sample-splitting, Uniform convergence

Citation Format(s)

False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation. / Du, Lilun; Guo, Xu; Sun, Wenguang et al.
In: Journal of the American Statistical Association, Vol. 118, No. 541, 2023, p. 607-621.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review